Vijay Srinivas Agneeswaran is a senior director of technology at Publicis Sapient. Vijay has spent the last 12 years creating intellectual property and building products in the big data area at Oracle, Cognizant, and Impetus, including building PMML support into Spark/Storm and implementing several machine learning algorithms, such as LDA and random forests, over Spark. He also led a team that build a big data governance product for role-based, fine-grained access control inside of Hadoop YARN and built the first distributed deep learning framework on Spark. Earlier in his career, Vijay was a postdoctoral research fellow at the LSIR Labs within the Swiss Federal Institute of Technology, Lausanne (EPFL). He is a senior member of the IEEE and a professional member of the ACM. He holds four full US patents and has published in leading journals and conferences, including _IEEE Transactions.

His research interests include distributed systems, cloud, grid, peer-to-peer computing, machine learning for big data, and other emerging technologies.

Vijay holds a bachelor’s degree in computer science and engineering from SVCE, Madras University, an MS (by research) from IIT Madras, and a PhD from IIT Madras.

Presentations

We illustrate how capsule networks can be industrialized:
1. Overview of capsule networks and how they help in handling spatial relationships between objects in an image. We also learn about how they can be applied to text analytics.
2. We show an implementation of recurrent capsule networks, which are useful in text analytics, especially for some tasks such as summarization or classification.

Alberto Andreotti is a senior data scientist on the Spark NLP team at John Snow Labs, where he is implementing state-of-the-art NLP algorithms on top of Spark. He has a decade of experience working for companies including Motorola, Intel, and Samsung and as a consultant, specializing in the field of machine learning. Alberto has written lots of low-level code in C/C++ and was an early Scala enthusiast and developer. A lifelong learner, he holds degrees in engineering and computer science and is working on a third in AI. Alberto was born in Argentina. He enjoys the outdoors, particularly hiking and camping in the mountains of Argentina.

Presentations

A lot of business data is still scanned or snapped documents. This is a real-world case study on reading, understanding, classifying, and acting on facts extracted from such image files - using state-of-the-art, open source, deep learning based OCR, NLP, and forecasting libraries at scale.

Stacy Ashworth is a registered nurse and chief clinical officer at SelectData. Stacy’s professional interests lie in the use of technology to improve the quality of care through better decision making. An accomplished speaker, she has served as a contributor to the Healthcare Informatics and Technology track of the 2016 Business and Health Administration Association meeting, performing research regarding the evaluation of glucose monitoring technologies for cost-effective and quality control/management of diabetes. She holds a master’s degree in healthcare administration with an emphasis in informatics. Postacute care, geriatrics, and coding may be her passions, but her love is firmly centered on her family of two lively teenagers, a spouse, and a couple of schnauzers to keep things interesting.

Presentations

A lot of business data is still scanned or snapped documents. This is a real-world case study on reading, understanding, classifying, and acting on facts extracted from such image files - using state-of-the-art, open source, deep learning based OCR, NLP, and forecasting libraries at scale.

Bahman is a Director of Data Science at Rakuten (7th largest internet company in the world), managing an AI organization with engineering and data science managers, data scientists, machine learning engineers, and data engineers, globally distributed across 3 continents and in charge of the end-to-end AI systems behind the Rakuten Intelligence suite of products. Bahman has built and managed engineering and data science teams across industry, academia, and the public sector in areas including digital advertising, consumer web, cybersecurity, and non-profit fundraising, where he has consistently delivered substantial business value. He has also designed and taught courses, led an interdisciplinary research lab, and advised several theses in the Computer Science department at Stanford University, where he also did his own PhD focused on large-scale algorithms and machine learning, topics on which he is a well-published author.

Presentations

TensorFlow 2.0 has landed!
During this session, you will learn all about TensorFlow 2.0's new features, usability enhancements, performance increases, and focus on developer productivity. We will use the TF 2.0 migration tool to transition a model from TensorFlow 1.x to 2.0, and deploy an end-to-end open-source machine learning model.

Ashish leads recommendation system teams for events and trends at Twitter. He focusses on building scalable ML & recommendation systems. Prior to that, he was a Senior Director of Data Science at Capital One. He used AI/ML to generate insights from vast amounts of data and build interesting B2B, B2C and Enterprise products. Previously, he co-founded GALE Partners and headed the Machine Learning group, building AI/ML based marketing automation products. He helped the company grow from 9 to 120 in 2 years and setup the India office. He has over 19 years of experience in the technology industry, an MBA from Kellogg School of Management and a B Tech from IITBHU.

Presentations

This talk gives insight into unique recommendation system challenges at Twitter’s scale and what makes this a fun and challenging task.

Carolina Barcenas is Vice President of Data Science and AI for Data Products at Visa. She is responsible for exploring and developing advanced ways for leveraging data to create business value for Visa through artificial intelligence techniques. Carolina is also Austin’s co-leader of Visa Women in Technology as well as the organizing force behind the community college intern program that focuses on non-traditional candidates.
She has worked both in industry as well as academia and has over 20 years of experience designing predictive analytical solutions in fintech. Prior to joining Visa, she spent 7 years at PayPal where she was responsible for managing the risk of small and medium e-commerce sellers.
She holds a Ph.D. in Applied Statistics from the Georgia Institute of Technology as a Fulbright Scholar.

Presentations

Artificial intelligence has revolutionized the way we live, work and play. Payments is no exception. With the help of AI, electronic payments have become more secure and convenient for consumers globally — regardless of currency or form factor.
In this talk, we explore a use case in which data and deep learning converge to root out malicious actors and make the payments ecosystem more secure.

Tzvika Barenholtz works at Intuit’s Data Science org and Intuit Futures, leading a team dedicated to advanced machine learning out Intuit’s office in Israel. Before joining Intuit he lead product teams at Facebook, Google and EMC.

Presentations

We will describe Intuit’s efforts to deploy Homomorphic Encryption (FHE) in Production, allowing models to be trained and run on encrypted data, and supporting Intuit’s commitment to the highest standard in data stewardship. This session will cover some of the optimizations and tricks that make FHE practical.

Dylan Bargteil is a data scientist in residence at the Data Incubator, where he works on research-guided curriculum development and instruction. Previously, he worked with deep learning models to assist surgical robots and was a research and teaching assistant at the University of Maryland, where he developed a new introductory physics curriculum and pedagogy in partnership with HHMI. Dylan studied physics and math at University of Maryland and holds a PhD in physics from New York University.

Presentations

This course is a non-technical overview of AI and data science. You’ll learn common techniques, how to apply them in your organization, and common pitfalls to avoid. Though this course, you’ll pick up the language and develop a framework to be able to effectively engage with technical experts and utilize their input and analysis for your business’s strategic priorities and decision making.

Michael Bauer is a senior software engineer at Sylabs who is an expert in Linux container technologies. At Sylabs, he is the lead engineer of the core services team, providing technical oversight and direction over products such as Singularity, SingularityPRO, and various Kubernetes integrations. Michael has been involved with the Singularity open source project for almost three years, first as a contributor and now as a project lead and maintainer. Over the past three years, he’s given talks about Singularity and Linux containers around the world at conferences such as ISC, SC, FOSDEM, and many others. Recently he has been exploring novel approaches to machine learning via container technology.

Presentations

Containerization technology can be used to build distributed, scalable, and complex neural networks by leveraging decoupled resource pools - pools that would not traditionally be amenable to such a task. Using Singularity, we demonstrate the approach by treating a container as a Decoupled Neural Interface to enable novel applications for neural networks which were previously impractical.

Mayukh Bhaowal is a director of product management at Salesforce Einstein working on automated machine learning. Previously, Mayukh worked at startups in the domain of machine learning and analytics. He served as head of product of ML platform startup Scaled Inference, backed by Khosla Ventures, and led product at ecommerce startup Narvar, backed by Accel. He was also a principal product manager at Yahoo and Oracle. Mayukh holds a master’s degree in computer science from Stanford University.

Presentations

With the shift from the digital revolution to the AI revolution, the old product management workflow and frameworks are crumbling down. How do you manage AI products, how are AI executive roles different and what toolbox do they need to succeed in the era of Artificial Intelligence?

Lukas Biewald is the founder of Weights and Biases. Before that he was the founder of CrowdFlower,a machine learning data labeling company acquired by Appen for 300 million dollars.. He was featured on Inc. magazine’s 30 under 30 list. Lukas holds a BS in mathematics and an MS in computer science from Stanford. He is also an expert Go player.

Presentations

Introduction to building and deploying LSTMs, GRUs and other text classification techniques using Keras and Scikit Learn.

Chris Butler is the Chief Product Architect at IPSoft. Chris has over 19 years of product and business development experience at companies like Microsoft, KAYAK, and Waze. He was first introduced to AI through graph theory and genetic algorithms while studying computer systems engineering at Boston University and has worked on AI-related projects at his startup Complete Seating (data science and constraint programming), Horizon Ventures (advising portfolio companies like Affectiva), and Philosophie (AI consulting and coaching). He has created techniques like empathy mapping for the machine and confusion mapping to create cross-team alignment while building AI products.

Presentations

Purpose, a well-defined problem, and trust from people are important factors to any system, especially those that employ AI. Chris Butler leads you through exercises that borrow from the principles of design thinking to help you create more impactful solutions and better team alignment.

Paris Buttfield-Addison is cofounder of Secret Lab, a game development studio based in beautiful Hobart, Australia. Secret Lab builds games and game development tools, including the multi-award-winning ABC Play School iPad games, the BAFTA- and IGF-winning Night in the Woods, the Qantas airlines Joey Playbox games, and the Yarn Spinner narrative game framework. Previously, Paris was mobile product manager for Meebo (acquired by Google). Paris particularly enjoys game design, statistics, the blockchain, machine learning, and human-centered technology research and writes technical books on mobile and game development (more than 20 so far) for O’Reilly. He holds a degree in medieval history and a PhD in computing.

Presentations

Are you a scientist who wants to test a research problem without building costly and complicated real-world rigs? A self-driving car engineer who wants to test their AI logic in a constrained virtual world? A data scientist who needs to solve a thorny real-world problem without touching a production environment? Have you considered simulation-driven ML problem solving with a game engine?

Are you a software engineer or scientist who wants to test a research problem without building costly and complicated real-world rigs? A self-driving car engineer who wants to test AI logic in a constrained virtual world? A data scientist who needs to solve a thorny real-world problem without touching a production environment? Have you considered AI using game engines? No? We'll teach you how.

Wei Cai was born in China. She received her master degrees in Actuarial Science and Business Anlysis from Georgia State University, Atlanta, Georgia. She is currently seeking master degree with the concentration on computing and analysis from Georgia Institute of Technology, Altana, Georgia, and is expected to graduate in 2019.

She joined Cox Communication, Atlanta, Georgia, in 2013. Since 2016, she made transition from building statistical model and doing analysis using R and SAS to developing machine learning models processing streaming telemetry data using python, scala, and Java. She successfully developed both statistical models and deep learning models for company to build a 10-year capacity planning budget. She is working on building models to compute to maximize network availability.

Her major areas of research interest are building deep learning models to help proactive network management.

Presentations

Real-time traffic volume prediction plays a vital role in proactive network management, and many forecasting models have been proposed to address this issue in the literature. However, most of them suffer from the inability to fully use the rich information in traffic data to generate efficient and accurate traffic predictions for a longer term (i.e., 7 day predictions at a 5-min interval).

Dr. David (Dave) Castillo leads Capital One’s Center for Machine Learning and Emerging Technology. In this role, Dave is responsible for driving excellence in Applied ML Research, University ML Research, ML technologies (tools and platforms), ML Consulting, and ML awareness within Capital One. Dave is a strong advocate of Responsible AI and has a keen interest in Automated Machine Learning and Timeseries ML.

For more than 25 years, Dr. Castillo has been involved with developing applications involving big data, artificial intelligence, machine learning, and large-scale distributed computing across a wide variety of industries. He has spent a great deal of time in Real-time ML for bidding on real-time auctions and delivering personalized advertising to online and mobile devices. He is a promoter of analyzing data streams “in flight” to extract meaningful content and for creating and delivering model features in near real-time. David is also experienced in developing and deploying fully automated self-learning models.

Dr. Castillo began his career developing artificial intelligence applications for NASA. He has since held positions as Chief Software Engineer for Motorola’s Iridium system, Chief Information Officer at KAST (an AI company), Chief Analytics Architect for Adenyo/Motricity, Chief Technology Officer at Voltari, and Chief Data Scientist for Early Warning Services. He has founded two startups in the areas of automated machine learning for online and mobile marketing and advertising.

Dr. Castillo holds a Bachelor’s in Engineering from the University of Arizona, a Master’s in Engineering from Arizona State University and earned a doctorate in Engineering from the University of Central Florida. He is an active speaker and participant in industry events and an Adjunct Professor of Computer Science at the University of Maryland University College.

Presentations

This talk will review Capital One's approach to explainable AI, with particular focus on fairness in automated decisioning. I will share our key learnings on best practices in implementing fair and responsible AI systems, as well as the challenges we have faced along the way and the research efforts we’ve initiated to overcome them.

Roger Chen is cofounder and CEO of Computable and program chair for the O’Reilly Artificial Intelligence Conference. Previously, he was a principal at O’Reilly AlphaTech Ventures (OATV), where he invested in and worked with early-stage startups primarily in the realm of data, machine learning, and robotics. Roger has a deep and hands-on history with technology. Before startups and venture capital, he was an engineer at Oracle, EMC, and Vicor. He also developed novel nanoscale and quantum optics technology as a PhD researcher at UC Berkeley. Roger holds a BS from Boston University and a PhD from UC Berkeley, both in electrical engineering.

Program Chairs, Ben Lorica and Roger Chen open the first day of keynotes.

Chiranjeet Chetia is a Lead Data Scientist at Visa. With the scale of data Visa observes every day, he is immersed into finding meaning and value from this data. To this end, he often collaborates with stake-holders across business and technology at Visa to conduct proof-of-concepts with the end goal of creating data & AI-powered products or services for Visa.
He has 10+ years of experience in the Payments domain, most of it in the realms of eCommerce. Prior to joining Visa, he had various stints from managing SMB merchant risk to managing Global Collections strategy at PayPal.
He holds an M.S. degree in Statistics from Virginia Tech where he also was a Provost Bioinformatics Fellow.

Presentations

Artificial intelligence has revolutionized the way we live, work and play. Payments is no exception. With the help of AI, electronic payments have become more secure and convenient for consumers globally — regardless of currency or form factor.
In this talk, we explore a use case in which data and deep learning converge to root out malicious actors and make the payments ecosystem more secure.

Michael Chui is a San Francisco-based partner in the McKinsey Global Institute, where he directs research on the impact of disruptive technologies, such as big data, social media, and the internet of things, on business and the economy. Previously, as a McKinsey consultant, Michael served clients in the high-tech, media, and telecom industries on multiple topics. Prior to joining McKinsey, he was the first chief information officer of the City of Bloomington, Indiana, and was the founder and executive director of HoosierNet, a regional internet service provider. Michael is a frequent speaker at major global conferences and his research has been cited in leading publications around the world. He holds a BS in symbolic systems from Stanford University and a PhD in computer science and cognitive science and an MS in computer science, both from Indiana University.

Presentations

AI has potential to create substantial value for business and the global economy. What’s less well understood is how it can be used to address some of the world’s biggest societal challenges. Michael Chui will discuss the ethical implications of AI and how executives can leverage the technology for good while considering its wide-reaching repercussions on business and human society alike.

Ira Cohen is a cofounder and chief data scientist at Anodot, where he is responsible for developing and inventing the company’s real-time multivariate anomaly detection algorithms that work with millions of time series signals. He holds a PhD in machine learning from the University of Illinois at Urbana-Champaign and has over 12 years of industry experience.

Presentations

The goal of the tutorial is to learn and experience what it takes to be a manage machine learning (ML ) based products. In the tutorial we will go through the cycle of developing machine learning based capabilities (or entire products) and the role of the (product) manager in each step of the cycle.

Sequence to Sequence (S2S) modeling using neural networks has been increasingly becoming mainstream in the recent years. In particular, it has been leveraged for applications such as, speech recognition, language translation and question answering. we shall walk through how S2S modeling can be leveraged for the aforementioned use cases, viz., real-time anomaly detection and forecasting.

Neil Conway is the co-founder and CTO of Determined AI, a startup building software to make deep learning developers dramatically more productive. Before founding Determined AI, Neil was a technical lead at Mesosphere and earned a PhD in computer science from UC Berkeley, where he performed research in distributed systems and large-scale data management. He has been a major contributor to several notable open source projects, including Apache Mesos and Postgres.

Presentations

Success with deep learning requires understanding more than just TensorFlow or Keras. In this tutorial, we will describe a range of practical problems faced by DL practitioners and the software tools and techniques needed to address them, including data prep/augmentation, GPU scheduling, hyperparameter tuning, distributed training, metrics management, deployment, and mobile/edge optimization.

Jason Dai is a senior principal engineer and chief architect for big data technologies at Intel, where he leads the development of advanced big data analytics, including distributed machine learning and deep learning. Jason is an internationally recognized expert on big data, the cloud, and distributed machine learning; he is the cochair of the Strata Data Conference in Beijing, a committer and PMC member of the Apache Spark project, and the creator of BigDL, a distributed deep learning framework on Apache Spark.

Presentations

Jason Dai, Yuhao Yang, Jennie Wang, and Guoqiong Song explain how to build and productionize deep learning applications for big data with Analytics Zoo—a unified analytics and AI platform that seamlessly unites Spark, TensorFlow, Keras, and BigDL programs into an integrated pipeline—using real-world use cases from JD.com, MLSListings, the World Bank, Baosight, and Midea/KUKA.

With more than 20 years of expertise in software, product development and data science, Leslie drives disruptive strategies and solutions, including AI and enterprise cloud solutions, to clients in a variety of markets — from healthcare and telco, to insurance, education and defense industries. Leslie is responsible for designing advanced deep learning, machine learning and chatbot solutions, including patented ground breaking products. One of her biggest strengths is team building, which is the foundation of repetition in the product creation process. In the course of her career, Leslie has held positions, such as Senior Software Product Architect; CTO; and VP, Product Development for key firms.

Presentations

The session will discuss a machine learning solution that is enabling the Puerto Rico Science, Technology and Research Trust to identify and classify disease-carrying mosquitoes. The presenter will outline the challenges, strategy and technologies utilized, the results achieved to date and implications of the AI project in helping to address a global threat.

Danielle Dean is a principal data scientist lead with the Azure Customer Advisory Team (AzureCAT) within the Cloud and AI organization, where she leads a team of data scientists and engineers building predictive analytics and machine learning solutions with external companies utilizing Microsoft’s Cloud AI Platform. Previously, she was a data scientist at Nokia, where she produced business value and insights from big data through data mining and statistical modeling on data-driven projects that impacted a range of businesses, products, and initiatives. Danielle holds a PhD in quantitative psychology from the University of North Carolina at Chapel Hill, where she studied the application of multilevel event history models to understand the timing and processes leading to events between dyads within social networks.

Presentations

In this session, you will learn best practices and reference architectures (which have been validated in real-world AI/ML projects for customers globally) for implementing AI. Join us in this session as Wee Hyong and Danielle share the lessons distil from working with large global customers on AI/ML projects, and the challenges that they overcome.

Madhura Dudhgaonkar is a Machine Learning leader at Workday passionate about modernizing the future of work. She is part of the Workday ML organization who is a pioneer in the Enterprise Machine Learning space, and has spent 5+ years building ML products leveraging Vision, Natural Language Processing, Recommendations, Anomaly Detection and more. Madhura’s career journey goes from being a hands-on engineer to leading large organizations across SUN Microsystems, Adobe and now Workday. Her background covers building consumer and enterprise products – latest of them involving multiple 0 to 1 product journeys leveraging Machine Learning. She is considered a thought leader in building ML products and is frequently invited to speak at AI conferences.

Madhura holds a master’s degree in math and computer science. When not obsessing over technology, she can be found outdoors, running, hiking or snowboarding.

Presentations

This session is a result of lessons learnt from productizing enterprise ML services across Vision, Language, Recommendations, Anomaly Detection over the last 5+ years. You will walk away with an actionable framework to bootstrap & scale a machine learning function. We highlight this via a real product journey involving deep learning that we productized in record speed in-spite of no dataset.

As Chief Executive Officer of Darktrace, Nicole Eagan has established the company as the global leader of AI cyber defense. Her extensive career spans 25 years working for Oracle and early to late-stage growth companies. Named ‘AI Leader of the Year’ in 2017, Nicole has introduced disruptive machine learning to enterprises of all sizes. Today, Darktrace has a valuation of $1.65 billion and counts Insight, KKR, Summit Partners, and Samsung among its investors. Darktrace’s innovative approach to cyber security has won more than 100 awards and the company has been named one of WSJ’s Tech Companies to Watch, Fast Company’s Most Innovative Companies, and the CNBC Disruptor 50.

Presentations

While nearly every firm is impacted by a wide variety of external factors, the most robust businesses are recognizing the need to first learn about themselves. Organizations are increasingly deploying self-learning AI. Able to learn how a company functions from the inside, and evolve with changes, this AI is enabling businesses to detect vulnerabilities, improve processes, and continue to grow.

Jana Eggers is CEO of the neuroscience-inspired artificial intelligence platform company Nara Logics. An experienced tech exec focused on inspiring teams to build great products, Jana has started and grown companies and led large organizations at public companies. She’s active in customer-inspired innovation, the artificial intelligence industry, the autonomy-mastery-purpose-style leadership, and running and triathlons. Previously, she held technology and executive positions at Intuit, Blackbaud, Los Alamos National Laboratory (computational chemistry and super computing), Basis Technology (internationalization technology), Lycos, American Airlines, Spreadshirt (ecomm), and startups that you’ve never heard of.

Presentations

Though we don’t always follow them, we have developed great best practices for designing, developing and delivering great software. What changes when we start adding AI to that great software? This talk will cover six key features of software dev that are similar when adding AI, and six that are different, and how to adjust for those.

Miro Enev is a senior solutions architect at NVIDIA specializing in advancing data science and machine intelligence while respecting human values. He supports the Pacific Northwest teams engaged with cloud and industrial and retail clients while participating in research in deep reinforcement learning and edge-to-cloud AI. Miro holds a Ph.D. from the University of Washington’s computer science and engineering department, where his thesis was on machine learning applications for information privacy in emerging sensor contexts. He studied cognitive science and computer science as an undergraduate at UC Berkeley.

Presentations

Machine learning (ML) and deep learning (DL) projects are becoming increasingly common at enterprises and startups alike and have been a key innovation engine for Amazon businesses such as Go, Alexa, and Robotics. In this 2 day training, Wenming Ye (AWS) and Miro Enev (Nvidia) offer a practical next step in DL learning with instructions, demos, and hands-on labs.

SANJIFERNANDO is a Vice President at OptumLabs, where he leads The Center for Applied Data Science (CADS). CADS focuses on the application of new data science methods to solve complex health care challenges by applying breakthrough innovations in artificial intelligence and machine learning to create software product concepts.

Sanji joined OptumLabs in 2014 from Nokia, where he was the Head of Data Science for Nokia’s Cloud Computing Group and HERE, Nokia’s navigation services division. Sanji spent 9 years at Nokia in a variety of roles with Nokia’s Multimedia Division, Nokia Research and Nokia Ventures.

Prior to Nokia, Sanji was a co-founder and VP of Engineering of a venture-backed mobile software company, Vettro. Sanji began his career in management consulting.

Sanji is a graduate of Trinity College with a bachelor’s degree in computer science. He lives in the Boston area with his wife and three boys. In his free time, Sanji enjoys coaching his sons in basketball and baseball.

Vijay obtained his PhD from IIT Bombay, India, 2012. Vijay subsequently joined IBM Research Labs, and worked on research and development of machine learning and deep learning in retail, telecom, and education domain. He started INFILECT in late 2015 to build computer-vision driven AI products for retail domain. Vijay has over 20 A* publications, and over 5 patents to his name. He is a frequent speaker at AI conferences (e.g., GTC 2018, San Jose, ACMKDD, 2018, London), and has won numerous awards for his contribution to advancements in AI.

Presentations

Beyond computer games and neural architecture search; practical applications of Deep Reinforcement Learning to improve classical classification or detection tasks are few and far between. In this talk, I will share a technique and our experiences of applying D-RL on improving the distribution input datasets to achieve state of the art performance, specifically on object detection tasks.

Anu Gali is an Engineering Leader at Uber leading Business Insights. Her team is responsible for Rides/Eats Trip Forecasting, helps business Optimize budget across Operations and Business units and predicts Customer Value across all our users. As a tech leader, Anu has built high performing engineering teams from the ground up and steered large-scale projects in Data, ML, Web, e-Commerce and Mobile technologies for companies such as Uber, Groupon, Shutterfly, and Adobe. Anu strongly believes in having an entrepreneurial mindset and in helping others to reach their potential. A few years back, in her spare time, she co-founded a company and released a social entertainment app “IntoMovies”. She also volunteers for a number of charitable organizations and STEM programs.

Presentations

Learn how Uber is leveraging AI to automate their business model via their unique platform. You'll hear about their technology that evolves based on current market insights and dynamically adjusts for the future. Anu Gali, Engineering Leader of this platform will discuss best practises and the architecture that enables organizations like Uber grow and scale rapidly.

Siddha Ganju, who Forbes featured in their 30 under 30 list, is a Self-Driving Architect at Nvidia. Previously at Deep Vision, she developed deep learning models for resource constraint edge devices. A graduate from Carnegie Mellon University, her prior work ranges from Visual Question Answering to Generative Adversarial Networks to gathering insights from CERN’s petabyte-scale data and has been published at top-tier conferences including CVPR and NeurIPS. Serving as an AI domain expert, she has also been guiding teams at NASA as well as featured as a jury member in several international tech competitions.

Presentations

Marina Rose Geldard, more commonly known as Mars, is a researcher from Down Under in Tasmania. Entering the world of technology relatively late as a mature-age student, she has found her place in the world: an industry where she can apply her lifelong love of mathematics and optimization. When she is not busy being the most annoyingly eager researcher ever, she compulsively volunteers at industry events, dabbles in research, and serves on the executive committee for her state’s branch of the Australian Computer Society (ACS). She’s currently writing “Practical AI with Swift” for O’Reilly Media.

Presentations

Are you a scientist who wants to test a research problem without building costly and complicated real-world rigs? A self-driving car engineer who wants to test their AI logic in a constrained virtual world? A data scientist who needs to solve a thorny real-world problem without touching a production environment? Have you considered simulation-driven ML problem solving with a game engine?

Are you a software engineer or scientist who wants to test a research problem without building costly and complicated real-world rigs? A self-driving car engineer who wants to test AI logic in a constrained virtual world? A data scientist who needs to solve a thorny real-world problem without touching a production environment? Have you considered AI using game engines? No? We'll teach you how.

Dr. Mazin Gilbert is the Vice President of Advanced Technology and Systems at AT&T Labs. He leads AT&T’s Research and Advanced Development of its network and access transformations. In this role, Mazin oversees advancements in artificial intelligence, software-defined networking and access, digital transformation, cloud technologies, open source software platforms and big data.
Mazin holds 176 U.S. patents in communication and multimedia processing and has published over 100 technical papers in human-machine communication. He is the author of the book titled, “Artificial Neural Networks for Speech Analysis/Synthesis,” 1992, and an editor of a recent book on “Artificial Intelligence for Autonomous Networks,” 2018.
With more than three decades of experience under his belt, Mazin’s previous work includes Bell Labs, BBC and British Telecom. He’s also worked in academia at Rutgers University, Princeton University and Liverpool University. He became an IEEE Fellow in 2012.
Mazin earned a bachelor’s and a doctoral degree, with first-class honors, in electrical engineering from the University of Liverpool. He also earned an MBA for Executives from the Wharton Business School of the University of Pennsylvania.
Outside of his technology career, Mazin is an entrepreneur owning five limited liability companies specializing in commercial and residential real estate and the dental industry. He also serves as a Chair of the Linux Foundation Deep Learning Foundation board, and a board member at the International Computer Science Institute (Berkeley). Mazin loves to spend time with his daughters and is an avid runner.

Presentations

This presentation will provide a technical and a market landscape overview of how AI is creating the 5G world. It will highlight how recent developments in AI are helping to accelerate widespread adoption of 5G-based applications for consumers and enterprises. We will discuss the roles of open source and open platforms as key ingredients of this 5G AI transformation.

Dylan Glas is a roboticist and researcher with over a decade of experience in the field of social human-robot interaction. He was a Guest Associate Professor at Osaka University and a Group Leader and Senior Researcher at the Advanced Telecommunications Research Institute (ATR) in Kyoto, Japan, where he developed frameworks and algorithms for multimodal perception, machine learning, and autonomous behavior generation for a variety of humanoid social robots. He has been featured on international media, including CBS, BBC, CNN, National Geographic, and The Guardian, for his work as the lead architect of ERICA, a highly-humanlike conversational android which is currently operating as a TV news anchor in Japan. He is currently Senior Robotics Software Architect at Futurewei Technologies in San Francisco.

Presentations

How do we develop social behavior for robots? Robot technologies are becoming more capable and affordable. Yet, even though technologies like natural language processing, mapping, and navigation are becoming more mature and standardized, it is often difficult to quantify human social behavior with algorithms. We highlight some of our researches in this field to enable human-robot interaction.

Enhao Gong is founder and CEO at Subtle Medical. He is a serial entrepreneur and PhD in Electrical Engineering at Stanford, with research focus on applying AI and deep learning to improve reconstruction, analysis and quantification in medical imaging . His work that applies AI to accelerate and reduce dose for MRI and PET has been featured in numbers of academic journals and clinical conferences. Dr. Gong won several awards including 2018 Forbes China/Asia 30-under-30 for his work at Subtle Medical, an AI+radiology startup from Stanford and the winner of 2018 NVIDIA Inception Award in AI+Healthcare.

Presentations

Subtle Medical provides AI solutions, cleared by FDA and powered by industry framework, such as Intel OpenVINO, to deliver 4x-10x faster MRI scans, 4x faster PET scans and up to 10x dosage reduction. Clinical evaluation at hospitals such as Hoag hospital, UCSF, and Stanford demonstrates the significant and immediate values of AI to improve the productivity of healthcare workflow.

Yael Gozin is a senior director at Pfizer. In her current role as a global clinical submission quality lead working with clinical development teams across Pfizer’s portfolio raised her interests in the use of technology to improve the processes and data quality associated with clinical development and regulatory submissions.

Yael provides technical guidance both in designing and implementing innovative Artificial Intelligence (AI) solutions to automate quality processes, and coaching and mentoring of project teams across different therapeutic areas including oncology, innovative pharma, established products, and consumer products.

She holds a doctorate from Swiss Federal Institute of Technology, Zurich (ETH Zurich) in organic chemistry and a master’s degree from the Weizmann Institute in organometallic chemistry.

Presentations

The process of matching and verifying a data point in a table cell with its accurate source(s) is one of the main challenges associated with automating data quality checks. Pfizer in partnership with Beaconcure developed an innovative, highly accurate and efficient structured data verification method.

Trevor Grant is committer on the Apache Mahout, and contributor on Apache Streams (incubating), Apache Zeppelin, and Apache Flink projects and Open Source Technical Evangelist at IBM. In former rolls he called himself a data scientist, but the term is so over used these days. He holds an MS in Applied Math and an MBA from Illinois State University. Trevor is an organizer of the newly formed Chicago Apache Flink Meet Up, and has presented at Flink Forward, ApacheCon, Apache Big Data, and other meetups nationwide.

Trevor was a combat medic in Afghanistan in 2009, and wrote an award winning undergraduate thesis between missions. He has a dog and a cat and a 64 Ford and he loves them all very much.

Presentations

Modeling is easy- productizing models, less so. Distributed training? forget about it. Hellllllloooo Kubeflow- a system that makes it easy for data scientists who know how to containerize their models, to train and serve on Kubernetes.

Joel Grus is a research engineer at the Allen Institute for Artificial Intelligence and the author of the beloved O’Reilly book Data Science from Scratch and the blog post “Fizz Buzz in TensorFlow.” Previously, he was a software engineer at Google and a data scientist at a variety of startups. He lives in Seattle.

Presentations

This tutorial will briefly discuss what modern neural NLP looks like, after which we'll train some models, write some code, and learn how you can apply these techniques to your own datasets and problems.

Dexter Hadley, MD, PhD has expertise in translating big data into precision medicine and digital health. His background is in genomics and computational biology and he has training in clinical pathology. His research generates, annotates, and ultimately reasons over large multi-modal data stores to identify novel biomarkers and potential therapeutics for disease. His early work resulted in a successful precision medicine clinical trial for ADHD (ClinicalTrials.gov Identifier: NCT02286817) for a first-in-class, non-stimulant neuromodulator to be targeted across the neuropsychiatric disease spectrum. More recently, his laboratory was funded by the NIH Big Data to Knowledge initiative to develop the stargeo.org online portal to crowd-source annotations of open genomics big data that allows users to discover the functional genes and biological pathways that are defective in disease. In addition to his genomics work, he develops state-of-the-art data driven models of clinical intelligence that drive clinical applications to more precisely screen, diagnose, and manage disease. Towards this end, he has been repeatedly recognized by UCSF with various awards including the inaugural UCSF Marcus Award for Precision Medicine to develop a digital learning health system to use smartphones to screen for skin cancer as well as a pilot award in precision imaging to better screen mammograms for invasive breast cancer. In general, the end point of his work is rapid proofs of concept clinical trials in humans that translate into better patient outcomes and reduced morbidity and mortality across the spectrum of disease.

Presentations

We will demonstrate how we use natural language processing techniques to curate routine clinical data for over 1M mammograms, and how we use deep learning, blockchain, and other approaches to realize AI that translates this valuable data into precision oncology to better characterize breast cancer and improve patient outcomes.

Kristian Hammond is Narrative Science’s chief scientist and a professor of computer science and journalism at Northwestern University. His research has been primarily focused on artificial intelligence, machine-generated content, and context-driven information systems. He currently sits on a United Nations policy committee run by the United Nations Institute for Disarmament Research (UNIDIR). Kris was also named 2014 innovator of the year by the Best in Biz Awards. He holds a PhD from Yale.

Presentations

Even as AI technologies move into common use, many enterprise decision makers remain baffled about what the different technologies actually do and how they can be integrated into their businesses. Rather than focusing on the technologies alone, Kristian Hammond provides a practical framework for understanding your role in problem solving and decision making.

Sijun He is a machine learning engineer at Twitter Cortex, where he works on content understanding with Deep Learning and NLP. Previous he was a data scientist at Autodesk. Sijun holds a MS in Statistics from Stanford University.

Presentations

Twitter is what’s happening in the world right now. To connect users with the best content, Twitter needs to build up a deep understanding of its noisy and temporal text content. Sijun He provides an overview of the Named Entity Recognition system at Twitter and discusses the challenges we face to build and scale a large-scale deep learning system to annotate 500 million tweets per day.

Bastiane Huang leads product strategy at Osaro, a San Francisco based machine learning company building Deep Reinforcement Learning software for industrial robots, backed by Peter Thiel and Jerry Yang’s AME Cloud. Bastiane has close to a decade of experience in the automation and manufacturing industries. Her experience in the field started in 2009 at e2v, a British space and industrial image sensor and machine vision camera manufacturer that is now part of Teledyne. She has broad experience in product marketing, business development, and operations at international technology companies across the industrial automation, IoT, AI, and robotics industries. She co-founded a software business at Advantech, the world’s biggest industrial computer manufacturer. The product offered video analytics solutions to improve traffic congestion and shopping experiences through people counting, and facial and heat map analysis. She was also an investor and advisor to early stage IoT and AI startups in the U.S. and Greater China, and previously worked as a Senior Product Manager at Amazon Alexa. In addition, she is actively involved with Harvard’s ‘Managing the Future of Work’ initiative on AI and robotics writing case studies and articles. Bastiane holds a B.S. in Information Management (2009) from National Taiwan University and an M.B.A in Technology and Entrepreneurship (2018) from Harvard Business School.

Presentations

Machine learning has enabled a move away from manually programming robots to allowing machines to learn/adapt to changes in the environment. We will discuss how AI-enabled robots are currently used in warehouse automation.
We will describe recent progress in DRL, imitation learning, etc. and discuss real world requirements for various industrial problems, pipelined versus end to end systems.

Wee Hyong Tok is a principal data scientist lead with the Azure Customer Advisory Team (AzureCAT) within the Cloud and AI organization. Wee Hyong has worn many hats in his career, including developer, program and product manager, data scientist, researcher, and strategist, and his track record of leading successful engineering and data science teams has given him unique superpowers to be a trusted AI advisor to customers. Wee Hyong coauthored several books on artificial intelligence, including Deep Learning with Azure, Predictive Analytics Using Azure Machine Learning.

Wee Hyong holds a PhD in computer science from the National University of Singapore.

Presentations

In this session, you will learn best practices and reference architectures (which have been validated in real-world AI/ML projects for customers globally) for implementing AI. Join us in this session as Wee Hyong and Danielle share the lessons distil from working with large global customers on AI/ML projects, and the challenges that they overcome.

Automated machine learning (AutoML) enables both data scientists and domain experts (with limited machine learning training) to be productive and efficient. AutoML is seen as a fundamental shift in which organizations can approach making machine learning. In this talk, you will learn how to use AutoML to automate selection of machine learning models and automate tuning of hyperparameters.

Amir Issaei is a data science consultant at Databricks, where he educates customers on how to leverage the company’s Unified Analytics Platform in machine learning (ML) projects. He also helps customers implement ML solutions and use advanced analytics to solve business problems. Previously, he worked in the Operations Research Department at American Airlines, where he supported the Customer Planning, Airport, and Customer Analytics Groups. He holds an MS in mathematics from the University of Waterloo and a BE in physics from the University of British Columbia.

Presentations

The course covers the fundamentals of neural networks and how to build distributed Keras/TensorFlow models on top of Spark DataFrames. Throughout the class, you will use Keras, TensorFlow, Deep Learning Pipelines, and Horovod to build and tune models. You will also use MLflow to track experiments and manage the machine learning lifecycle. NOTE: This course is taught entirely in Python.

Ram Janakiraman is a Distinguished Engineer at the Aruba CTO Office working on Machine Intelligence for Enterprise Security. Ram’s recent focus has been on simplifying building of behavior models by leveraging approaches in NLP and Representation learning. He hopes to improve end-user product engagement through a visual representation of entity interactions. Ram has numerous patents in a variety of areas during the course of his career.

Ram has been in various startups and was a co-founding member of Niara Inc working on security analytics with a focus on threat detection and investigation before it was acquired by Aruba, an HPE Company. Ram is an avid Scuba Diver always eager to explore the next reef or kelp. He is also an FAA Certified Drone Pilot capturing the beauty of dive destinations on his trips.

Presentations

While network protocols are the language of the conversations among devices in a network, these conversations are hardly ever labeled. Advances in embeddings to capture semantics, even that of polysemous words, presents an opportunity for capturing access semantics to model user behavior. With strong embeddings as a foundation, behavioral use-cases could be mapped to NLP models of choice.

Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. His research interests bridge the computational, statistical, cognitive, and biological sciences; in recent years, he has focused on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines, and applications to problems in distributed computing systems, natural language processing, signal processing, and statistical genetics. Previously, he was a professor at MIT. Michael is a member of the National Academy of Sciences, the National Academy of Engineering, and the American Academy of Arts and Sciences and a fellow of the American Association for the Advancement of Science, the AAAI, ACM, ASA, CSS, IEEE, IMS, ISBA, and SIAM. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009. Michael holds a master’s degree in mathematics from Arizona State University and a PhD in cognitive science from the University of California, San Diego.

Presentations

Holden Karau is a transgender Canadian open source developer advocate at Google focusing on Apache Spark, Beam, and related big data tools. Previously, she worked at IBM, Alpine, Databricks, Google (yes, this is her second time), Foursquare, and Amazon. Holden is the coauthor of Learning Spark, High Performance Spark, and another Spark book that’s a bit more out of date. She is a committer on the Apache Spark, SystemML, and Mahout projects. When not in San Francisco, Holden speaks internationally about different big data technologies (mostly Spark). She was tricked into the world of big data while trying to improve search and recommendation systems and has long since forgotten her original goal. Outside of work, she enjoys playing with fire, riding scooters, and dancing.

Presentations

Modeling is easy- productizing models, less so. Distributed training? forget about it. Hellllllloooo Kubeflow- a system that makes it easy for data scientists who know how to containerize their models, to train and serve on Kubernetes.

Meher is a seasoned software developer with apps used by tens of millions of users every day. Currently at Square, and previously at Microsoft, he shipped features for a range of apps, from Square’s Point of Sale to the Bing app. He was the mobile development lead for Microsoft’s Seeing AI app, which has received widespread recognition and awards from Mobile World Congress, CES, FCC, American Council of the Blind to name a few. A hacker at heart with a flair for fast prototyping, he has won close to two dozen hackathons and converted them to features shipped in widely-used products. He also serves as a judge of international competitions including Global Mobile Awards, Edison Awards.

Presentations

Until recently, Arun Kejariwal was a statistical learning principal at Machine Zone (MZ), where he led a team of top-tier researchers and worked on research and development of novel techniques for install and click fraud detection and assessing the efficacy of TV campaigns and optimization of marketing campaigns. In addition, his team built novel methods for bot detection, intrusion detection, and real-time anomaly detection. Previously, Arun worked at Twitter, where he developed and open-sourced techniques for anomaly detection and breakout detection. His research includes the development of practical and statistically rigorous techniques and methodologies to deliver high-performance, availability, and scalability in large-scale distributed clusters. Some of the techniques he helped develop have been presented at international conferences and published in peer-reviewed journals.

Presentations

Sequence to Sequence (S2S) modeling using neural networks has been increasingly becoming mainstream in the recent years. In particular, it has been leveraged for applications such as, speech recognition, language translation and question answering. we shall walk through how S2S modeling can be leveraged for the aforementioned use cases, viz., real-time anomaly detection and forecasting.

Mayank Kejriwal is a research lead and research assistant professor at the USC Information Sciences Institute, where he conducts research on multiple Department of Defense-funded AI projects, on topics ranging from knowledge graphs to deploying AI for global social impact. Mayank has given talks in over 30+ academic and industrial venues, and was on the Forbes 30 under 30 (2019) shortlist in the Science category. He is currently co-authoring a textbook on knowledge graphs, and his work on using AI to combat human trafficking has been featured in multiple press outlets.

Presentations

Embeddings have emerged as an exciting by-product of the deep neural revolution, and now apply universally to images, words, documents and graphs. Many algorithms only require unlabeled datasets, which are plentiful in businesses. This talk, geared towards management and executives, will describe what these embeddings really are and how businesses can use them to bolster their AI strategy.

Abhishek Kumar is a Senior Manager, Data science in Sapient’s Bangalore office, where he looks after scaling up the data science practice by applying machine learning and deep learning techniques to domains such as retail, ecommerce, marketing, and operations. Abhishek is an experienced data science professional and technical team lead specializing in building and managing data products from conceptualization to deployment phase and interested in solving challenging machine learning problems. Previously, he worked in the R&D center for the largest power-generation company in India on various machine learning projects involving predictive modeling, forecasting, optimization, and anomaly detection and led the center’s data science team in the development and deployment of data science-related projects in several thermal and solar power plant sites. Abhishek is a technical writer and blogger as well as a Pluralsight author and has created several data science courses. He is also a regular speaker at various national and international conferences (including strata conference ) and universities. Abhishek holds a master’s degree in information and data science from the University of California, Berkeley.

Presentations

We illustrate how capsule networks can be industrialized:
1. Overview of capsule networks and how they help in handling spatial relationships between objects in an image. We also learn about how they can be applied to text analytics.
2. We show an implementation of recurrent capsule networks, which are useful in text analytics, especially for some tasks such as summarization or classification.

Akhilesh is a senior machine learning engineer at Adobe. He works in applied machine learning team at Adobe which is primarily responsible for putting deep learning models in production. Part of his job is to train, evaluate and put deep learning models in scalable systems. He is an avid reader and loves to come up with solution for wide variety of problems.

Presentations

Photographic defects such as noise, exposure(underexposure/overexposure), blur can ruin the perfect shot. We have developed a solution based on GAN that can identify the region of defectiveness in images and fix these defective images. This solution is better than traditional algorithms. It can also be applied to fix videos. No more spending hours of time manually editing the images.

Tolga Kurtoglu is CEO of PARC, a Xerox company, which provides custom R&D services, technology, specialized expertise, best practices, and intellectual property to Xerox’s business groups, Fortune 500 and Global 1000 companies, startups, and government. Tolga oversees PARC’s R&D investments for Xerox and its innovation portfolio for commercial clients and government agencies in a diverse set of focus areas and competencies, including human-centered innovation services, intelligent agents and systems, clean energy, smart packaging, machine learning and analytics, security and privacy, printed electronics, and digital manufacturing. In his early years at PARC, he pioneered the formation of PARC’s digital design and manufacturing (DDM) program. Later he created and led the System Sciences Laboratory, building a technology portfolio across hardware, software, and process technologies. In both roles, he managed multimillion-dollar R&D investments and product strategy encompassing several platforms and market offerings and led successful transition of inventions from an R&D output to commercial software systems and services. Prior to PARC, he was a researcher at NASA’s Ames Research Center and a mechanical design engineer at Dell Corporation.

Tolga’s research focuses on computation and artificial intelligence applied to design and manufacturing of complex systems, and application of preventive and predictive analytics techniques to engineered systems. He has published over 80 peer-reviewed articles and papers in leading journals and conferences in his field and regularly serves in organizational leadership roles for the ASME, AIAA, AAAI, Design Society, and Prognostics and Health Management Society. He is the recipient of the IEEE Best Professional Paper Award at the Prognostics and Health Management Conference, IEEE Best Application Paper Award from IEEE Robotics and Automation Society, NASA Ames Technical Excellence Award, PARC Excellence Award, PARC Golden Acorn Award, and the Best Design Award in “Dexterous Robot Hand” Design Competition. Tolga holds a PhD from the University of Texas at Austin, an MS from Carnegie Mellon University, and a bachelor’s degree from Orta Dogu Technical University (ODTU)—all in mechanical engineering.

Presentations

The use of AI is growing rapidly and expanding into applications that impact people’s lives. Researchers have an obligation to consider the impact of intelligent applications. Assumptions made in the design process can be based on subjective value judgments. PARC is therefore leading an industry initiative to build ethical frameworks into the research and design of decision-making processes in AI.

Danny Lange is vice president of AI and machine learning at Unity Technologies, where he leads multiple initiatives around applied artificial intelligence. Previously, Danny was head of machine learning at Uber, where he led the efforts to build a highly scalable machine learning platform to support all parts of Uber’s business, from the Uber app to self-driving cars; general manager of Amazon Machine Learning, where he provided internal teams with access to machine intelligence and launched an AWS product that offers machine learning as a cloud service to the public; principal development manager at Microsoft, where he led a product team focused on large-scale machine learning for big data; CTO of General Magic, Inc.; and founder of his own company, Vocomo Software, where he worked on General Motor’s OnStar Virtual Advisor, one of the largest deployments of an intelligent personal assistant until Siri. Danny started his career as a computer scientist at IBM Research. He is a member of ACM and IEEE Computer Society and has numerous patents to his credit. Danny holds an MS and PhD in computer science from the Technical University of Denmark.

Presentations

This year, Unity introduced Obstacle Tower, a procedurally generated game environment designed to test the capabilities of AI-trained agents. Unity then invited the public to attempt to solve the challenge. Find out what the company learned from the contest and understand the real-world impact that can result from observing behaviors of multiple AI agents in a simulated virtual environment.

Francesca Lazzeri, PhD is an AI and machine learning scientist at Microsoft. Francesca has multiple years of experience as data scientist and data-driven business strategy expert; she is passionate about innovations in big data technologies and the applications of machine learning-based solutions to real-world problems. Her work on these issues covers a wide range of industries, including energy, oil and gas, retail, aerospace, healthcare, and professional services. Previously, she was a research fellow in business economics at Harvard Business School, where she performed statistical and econometric analysis within the Technology and Operations Management Unit and worked on multiple patent data-driven projects to investigate and measure the impact of external knowledge networks on companies’ competitiveness and innovation. Francesca is a mentor for PhD and postdoc students at the Massachusetts Institute of Technology and enjoys speaking at academic and industry conferences to share her knowledge and passion for AI, machine learning, and coding. Francesca holds a PhD in innovation management.

Presentations

Automated machine learning (AutoML) enables both data scientists and domain experts (with limited machine learning training) to be productive and efficient. AutoML is seen as a fundamental shift in which organizations can approach making machine learning. In this talk, you will learn how to use AutoML to automate selection of machine learning models and automate tuning of hyperparameters.

Dr. Li Erran Li is the chief scientist at Pony.ai and an adjunct professor at Columbia University. Prior to joining Pony.ai, he was with the perception team at Uber ATG and machine learning platform team at Uber. There, Erran worked on deep learning for autonomous driving, led the machine learning platform team technically and drove strategy for company-wide artificial intelligence initiatives. Before Uber, Erran worked at Bell Labs. Dr. Li’s current research interests are machine learning, computer vision, learning-based robotics and their application to autonomous driving. Dr. Li has a PhD from the Computer Science Department at Cornell University. Dr. Li is an IEEE Fellow and an ACM Fellow.

Presentations

Tremendous progresses have been made in applying machine learning to autonomous driving. I will present recent advances in applying machine learning to solving the perception, prediction, planning and control problems of autonomous driving. I will discuss key research challenges.

Tianhui Michael Li is the founder and CEO of the Data Incubator. Michael has worked as a data scientist lead at Foursquare, a quant at D.E. Shaw and JPMorgan, and a rocket scientist at NASA. At Foursquare, Michael discovered that his favorite part of the job was teaching and mentoring smart people about data science. He decided to build a startup that lets him focus on what he really loves. He did his PhD at Princeton as a Hertz fellow and read Part III Maths at Cambridge as a Marshall scholar.

Presentations

This course is a non-technical overview of AI and data science. You’ll learn common techniques, how to apply them in your organization, and common pitfalls to avoid. Though this course, you’ll pick up the language and develop a framework to be able to effectively engage with technical experts and utilize their input and analysis for your business’s strategic priorities and decision making.

Yunyao Li is a Senior Research Manager with IBM Research – Almaden, where she manages the Scalable Knowledge Intelligence department. She is a Master Inventor and a member of IBM Academy of Technology. She is also a member of the New Voices program of the American National Academies. Her expertise is in the interdisciplinary areas of natural language processing, databases, human-computer interaction, and information retrieval. Her contributions in these areas have resulted in 50+ research publications at top AI conferences/journals, 20+ patent filings and recognized by multiple prestigious IBM internal awards. She received her PhD degree in Computer Science & Engineering and dual master degrees in Computer Science & Engineering and Information Science from the University of Michigan, Ann Arbor and undergraduate degrees from Tsinghua University, Beijing, China. Yunyao is also deeply passionate about improving the diversity for the STEM field. She has been actively mentoring women and under-represented minorities both within and outside of IBM. She currently leads the Almaden Women’s Interest Network Group (AWING) at IBM.

Presentations

Natural Language Understanding (NLU) underlies a wide range of applications and services. Rich resources available for English do not exist for most other languages. Is it possible to avoid duplicating the effort? Further, can NLU-dependent applications be developed language-agnostically (write once, applicable to multiple languages)? We will show a vision to answering yes to both questions.

I am a physicist/mathematician turned computer scientist, and now a machine learning enthusiast. Through years of working as a data scientist, I develop and deploy machine learning solutions to solve real world business problems, such as using LSTM to forecast staffing needs, using xgboost models to execute real-time online customer behavior classifications. We live in an amazing era, where machine learning algorithms conceived some decades ago can be put into reality with a few lines of python code. As one of the first two data scientists in company history to join American Tire Distributors, I helped grow the data science team to a size of 12 within a year; and we are now developing machine learning solutions to help the company in supply chain, sales, warehousing, as well as eCommerce.

Presentations

Deep Learning has been a sweeping revolution in the world of AI and machine learning. But how does this new, hot, technology help a legacy business everyday? In this talk, I will go over a warehouse staffing solution we deployed in 140 distribution centers, where I implemented LSTM recurrent neural network model to generate staffing level forecasts and to optimize staffing schedules.

Kai Liu is a Senior Program Manager in AI and Research group of Microsoft. He has 7 years of experience in data driven engineering, big data platform and AI infrastructure for Office product families. He led his team to create a service health portal for SharePoint Online, inject a distributed log collection and storage system for Exchange Online, publish curated data sets and key business metrics and enable sub-hour experimentations in Office 365. Currently he is working on the AI and Deep Learning infrastructure for large scale enterprise data under compliance obligations.

Presentations

FrameworkLauncher is built to orchestrate all kinds of workloads on YARN through the same interface without making changes to the workload themselves.
These workloads include but not limited to: Large-Scale Long-Running Services (DeepLearning Serving, HBase, Kafka, etc), Batch Jobs (DeepLearning Training, KDTree Building, etc) and Streaming Jobs (Data Processing, Dynamic Rendering, etc).

Phoebe Liu is currently a machine learning scientist at Figure Eight, an AI and machine-learning startup based in San Francisco. Previously, she was a robotics researcher in Japan, working in Hiroshi Ishiguro Laboratory at Advanced Telecommunications Research Institute International (ATR). At the same time, she earned her Ph.D. at Osaka University (2017). She was involved in projects including enabling conversational social robot to imitate human behaviors, android science, and teleoperation system for semi-autonomous robot.

Presentations

How do we develop social behavior for robots? Robot technologies are becoming more capable and affordable. Yet, even though technologies like natural language processing, mapping, and navigation are becoming more mature and standardized, it is often difficult to quantify human social behavior with algorithms. We highlight some of our researches in this field to enable human-robot interaction.

Ben Lorica is the chief data scientist at O’Reilly Media. Ben has applied business intelligence, data mining, machine learning, and statistical analysis in a variety of settings, including direct marketing, consumer and market research, targeted advertising, text mining, and financial engineering. His background includes stints with an investment management company, internet startups, and financial services.

Program Chairs, Ben Lorica and Roger Chen open the first day of keynotes.

Boris Lublinsky is a software architect at Lightbend, where he specializes in big data, stream processing, and services. Boris has over 30 years’ experience in enterprise architecture. Over his career, he has been responsible for setting architectural direction, conducting architecture assessments, and creating and executing architectural roadmaps in fields such as big data (Hadoop-based) solutions, service-oriented architecture (SOA), business process management (BPM), and enterprise application integration (EAI). Boris is the coauthor of Applied SOA: Service-Oriented Architecture and Design Strategies, Professional Hadoop Solutions, and Serving Machine Learning Models. He is also cofounder of and frequent speaker at several Chicago user groups.

Presentations

This hands-on tutorial examines production use of ML in streaming data pipelines; how to do periodic model retraining and low-latency scoring in live streams. We'll discuss Kafka as the data backplane, pros and cons of microservices vs. systems like Spark and Flink, tips for Tensorflow and SparkML, performance considerations, model metadata tracking, and other techniques.

Hagay Lupesko is part of the deep learning leadership team at Amazon Web Services, and currently works to democratize Artificial Intelligence and Deep Learning through cloud services and open source projects such as MXNet and ONNX. He has been busy building software for the past 15 years, and still enjoys every bit of it (literally)! He engineered and shipped products across various domains: from 3D cardiac imaging with real time in-vessel tracking, through semi-conductors fab systems that measures structures the size of molecules, and up to web-scale systems with global distribution.

Presentations

In this session, you will learn how Lex, Amazon's cloud-based AI-powered chatbot service, was architected, built and deployed. You will learn practical considerations for deploying and maintaining deep learning models in production, and how Lex used Apache MXNet and MXNet Model Server to build and scale the successful service.

Chaithanya is an Assistant Vice President at EXL Service. He has over 10 years of experience in developing advanced analytics solutions across multiple business domains. He holds a bachelor of technology degree from IIT Guwahati. At EXL,he is responsible for building AI enabled solutions which can bring efficiencies across various business processes

Presentations

Every NLP based document processing solution depends on converting scanned documents/ images to machine readable text using an OCR solution. However, accuracy of OCR solutions is limited by quality of scanned images. We show that generative adversarial networks can be used to bring significant efficiencies in any document processing solution by enhancing resolution and de-noising scanned images.

James Manyika is a Chairman of the McKinsey Global Institute, McKinsey & Company’s business and economics research arm, and one of its three global coleaders. James has led research on business and global economic trends, including the digital economy, globalization, growth and productivity, innovation and competitiveness, and labor markets. James is also a director at McKinsey, where he is one of the leaders of McKinsey’s Global High Tech, Media, and Telecom practice. Based in Silicon Valley for 20 years, he has worked with many of the world’s leading technology companies on a variety of issues. He has published a book on distributed networks and robotics another on globalization, as well as numerous academic and business papers and reports. In 2012, James was appointed by President Obama to serve on of the US President’s Global Development Council and in 2013 to serve as the vice chairman of the council. In 2011, James was appointed by the US secretary of commerce to serve on the Innovation Advisory Board as part of the COMPETES Act.

James serves on the boards of the Aspen Institute, the Oxford Internet Institute, UC Berkeley’s School of Information, Harvard’s Hutchins Center for African and African American Research, including the W. E. B. Du Bois Institute, and the School of Global Affairs and Public Policy at the American University in Cairo. James is a nonresident senior fellow of the Brookings Institution, a member of the Council on Foreign Relations, and a member of the Bretton Woods Committee. James was on the engineering faculty at Oxford University and a fellow at Balliol College, Oxford University, a visiting scientist at NASA’s Jet Propulsion Laboratory, and a faculty exchange fellow at MIT. A Rhodes Scholar, James holds DPhil, MSc, and MA degrees from Oxford in engineering, mathematics, and computer science respectively and a BSc in electrical engineering from the University of Zimbabwe.

Presentations

AI has potential to create substantial value for business and the global economy. What’s less well understood is how it can be used to address some of the world’s biggest societal challenges. Michael Chui will discuss the ethical implications of AI and how executives can leverage the technology for good while considering its wide-reaching repercussions on business and human society alike.

Milojicic is a distinguished technologist at Hewlett Packard Labs, Palo Alto, CA (1998-) leading system software teams in US, Brazil, and India. He worked at the OSF Research Institute in Cambridge, MA and at the Mihajlo Pupin Institute in Belgrade, Serbia. Milojicic received his PhD from Kaiserslautern University, Germany; and his MSc/BSc from Belgrade University, Serbia. He was a technical director of the Open Cirrus Cloud Computing Testbed, with academic, industrial and government sites in the US, Europe, and Asia. He has published 2 books and 180 papers; he has 31 granted patents. He is an IEEE Fellow (2010) and ACM Distinguished Engineer (2008). Milojicic was on 8 PhD thesis committees and taught Cloud management course at SJSU. As president of the IEEE Computer Society (2014), he started Tech Trends, the top viewed CS news. As the IEEE industry engagement chair, he started IEEE Infrastructure’18 conference dedicated for industry.

Presentations

We developed a software stack for the special purpose machine learning accelerator. Software stack improves usability and programmability of the accelerator, making it accessible from common Machine Learning frameworks. Software toolchain also exposes the intricacies of the parallelism of the accelerator while hiding its complexities.

Philipp Moritz is a PhD candidate in EECS at UC Berkeley, with broad interests in artificial intelligence, machine learning, and distributed systems. He is a member of the Statistical AI Lab and the RISELab.

Presentations

Ray is a general purpose framework for programming your cluster. We will lead a deep dive into Ray, walking you through its API and system architecture and sharing application examples, including several state-of-the-art AI algorithms.

Srinivas Narayanan leads Applied Research at Facebook AI. The team does research and development in a wide range of areas such as PyTorch, Computer Vision, Natural Language, Speech and Personalization to push the state of the art in AI to advance Facebook products. He has led several major efforts at Facebook including creating the Interest Graph, launching the Location product, and leading engineering for Photos where he also helped start Facebook’s efforts in Computer Vision and Deep Learning. Prior to Facebook, has was founding member of two startups and was part of the database systems research group at IBM Almaden Research Center.

Presentations

This session will take a deeper look into the next change we are seeing in AI - going beyond fully supervised learning techniques.

Paul Nemitz is principal adviser of the European Commission on strategic justice issues. Previously, he was director for human rights and citizenship, leading reform of privacy law in Europe, and lead negotiator of the EU-US Privacy Shield Framework and of the code of conduct against hate speech and incitement to violence on the internet.

Presentations

European laws already regulate AI. The General Data Protection Regulation (GDPR) is one example. And there are two processes under way, which may lead to new law, specific to AI: The German Data Ethics Commmission and the European Union High Level Group on AI. The speaker participates in both groups and will analyze the impact of existing and new rules on AI development and deployment globally.

Robert Nishihara is a fourth-year PhD student working in the UC Berkeley RISELab with Michael Jordan. He works on machine learning, optimization, and artificial intelligence.

Presentations

Ray is a general purpose framework for programming your cluster. We will lead a deep dive into Ray, walking you through its API and system architecture and sharing application examples, including several state-of-the-art AI algorithms.

Tim Nugent pretends to be a mobile app developer, game designer, tools builder, researcher, and tech author. When he isn’t busy avoiding being found out as a fraud, Tim spends most of his time designing and creating little apps and games he won’t let anyone see. He also spent a disproportionately long time writing his tiny little bio, most of which was taken up trying to stick a witty sci-fi reference in…before he simply gave up.

Presentations

Are you a scientist who wants to test a research problem without building costly and complicated real-world rigs? A self-driving car engineer who wants to test their AI logic in a constrained virtual world? A data scientist who needs to solve a thorny real-world problem without touching a production environment? Have you considered simulation-driven ML problem solving with a game engine?

Are you a software engineer or scientist who wants to test a research problem without building costly and complicated real-world rigs? A self-driving car engineer who wants to test AI logic in a constrained virtual world? A data scientist who needs to solve a thorny real-world problem without touching a production environment? Have you considered AI using game engines? No? We'll teach you how.

Debo Olaosebikan is an entrepreneur & engineer based in San Francisco. Debo has founded multiple marketplace, energy, and AI startups and is on leave from a Physics PhD at Cornell where he was lead researcher from Cornell, on an MIT/ Department of Defense project to build the world’s first electric silicon laser.

Through various projects and startups in Nigeria and the US he has worked on labor marketplaces, AI for software testing & medical diagnoses, NLP-based query and recommendation engines, carbon nanotube theory, spintronics, nanophotonics, interactive books and authoring tools, silicon lasers, collaborative data science systems and optical tweezers.

He was once a radio featured musician and was the young Nigerian scientist of 2011. Upon taking a leave from his PhD., Debo moved out to Silicon Valley and along with Roger Dickey founded Gigster, an on-demand service for software development & design with a network of 1000 top engineers, product managers and designers. Gigster is backed by Andreessen Horowitz, Redpoint, Greylock, and Y Combinator. Debo advises startups and helps young founders as a mentor at the Thiel Fellowship.

Presentations

As the gap between technology giants and the rest of the enterprise widens, AI driven transformation has become essential and urgent. From the lens of over 1000 projects delivered and a broad view across real use cases in multiple industries, I will present a organizational and technical framework for using AI to drive true business impact regardless of where an organization is starting from

Richard Ott is a data scientist in residence at the Data Incubator, where he gets to combine his interest in data with his love of teaching. Previously, he was a data scientist and software engineer at Verizon. Rich holds a PhD in particle physics from the Massachusetts Institute of Technology, which he followed with postdoctoral research at the University of California, Davis.

Presentations

PyTorch is a machine learning library for Python that allows users to build deep neural networks with great flexibility. Its easy to use API and seamless use of GPUs make it a sought after tool for deep learning. This course will introduce the PyTorch workflow and demonstrate how to use it. Students will be equipped with the knowledge to build deep learning models using real-world datasets.

Alex Palladini is Innovation Leader at Music Tribe UK where he oversees the research in various areas like human-computer interaction, machine learning and AI as well as the development of new products based on AI.

His research in the field of AI is focused on the user experience of intelligent systems for complex and creative applications.

Presentations

What is the role of experts and creatives in a world dominated by intelligent machines?
In this presentation we will try to answer to this question by bridging the gap between the research on complex systems and tools for creativity, discussing what we believe to be the key design principles and perspective on the making of intelligent tools for creativity and for experts in the loop.

Mo Patel is an independent deep learning consultant advising individuals, startups, and enterprise clients on strategic and technical AI topics. Mo has successfully managed and executed data science projects with clients across several industries, including cable, auto manufacturing, medical device manufacturing, technology, and car insurance. Previously, he was practice director for AI and deep learning at Think Big Analytics, a Teradata Company, where he mentored and advised Think Big clients and provided guidance on ongoing deep learning projects; he was also a management consultant and a software engineer earlier in his career. A continuous learner, Mo conducts research on applications of deep learning, reinforcement learning, and graph analytics toward solving existing and novel business problems and brings a diversity of educational and hands-on expertise connecting business and technology. He holds an MBA, a master’s degree in computer science, and a bachelor’s degree in mathematics.

Presentations

This tutorial will focus on all aspects of the PyTorch lifecycle via hand on examples such as image classification, text classification, and linear modeling. Other aspects of machine learning such as transfer learning, data modeling and deploying to production will be covered via immersive labs.

A full-stack developer with two decades of industry experience, Jon Peck now focuses on bringing scalable, discoverable, and secure machine-learning microservices to developers across a wide variety of platforms via Algorithmia.com

Presentations

We’ll look at why Machine Learning is a natural fit for serverless computing, discuss a general architecture for scalable ML, and cover issues we ran into when implementing our own on-demand scaling over GPU clusters, providing general solutions and a vision for the future of cloud-based ML

Justina has a background in Econometrics and Data Analytics. Her curiosity for Data Science and human behaviour analytics has taken her to many places and industries – over the past three years she has been doing Data Science work across video gaming, fintech, insurance industries. Her interest in chatbots, natural language processing and open source has led her to Rasa, a Berlin-based conversational AI startup where she works as a Developer Advocate focusing on improving developer experience in using open source software for conversational AI.

Presentations

In this workshop, you will get hands-on experience in developing intelligent AI assistants based entirely on machine learning and using only open source tools - Rasa NLU and Rasa Core. You will learn the fundamentals of conversational AI and the best practices of developing AI assistants that scale and learn from real conversational data.

Vadim Pinskiy is the VP of Research and Development at Nanotronics, where he oversees product development, short term R&D and long term development of AI platforms. Vadim completed his doctorate work in Neuroscience, focused on mouse neuroanatomy using high throughput whole slide imaging and advanced tracing techniques. Prior to that, completed Masters in Biomedical Engineering from Cornell and Bachelor’s and Master’s in Electrical and Biomedical from Stevens Institute of Technology. Vadim is interested in applying advanced AI methods and systems to solving practical problems in biological and product manufacturing.

Presentations

We have developed a system that is capable of detecting, classifying and automatically correcting for manufacturing defects in a multinodal process

Dulce Ponceleón is a Principal Research Staff Member in the Infrastructure for Intelligent Information Systems group at IBM Research-Almaden. Her broad interests across different disciplines include natural language processing, machine learning, blockchain, and security. She has worked in information retrieval, multimedia content analysis, video summarization, speech recognition, numerical linear algebra, non-linear programming, storage systems, and content protection. She led IBM’s Content Protection team resulting in significant contributions to Blu-ray Content Protection Standard’s consortium. While at Apple Computer, Inc. she was a key contributor to QuickTime Conferencing’s video and audio compression. She received her Master and Ph.D. degree in Computer Science from Stanford University. She earned her B.S. degree (Cum Laude) in Computer Science from Universidad Simon Bolivar, Caracas, Venezuela.

Presentations

Natural Language Understanding (NLU) underlies a wide range of applications and services. Rich resources available for English do not exist for most other languages. Is it possible to avoid duplicating the effort? Further, can NLU-dependent applications be developed language-agnostically (write once, applicable to multiple languages)? We will show a vision to answering yes to both questions.

Shashank Prasanna is an AI & Machine Learning Evangelist at Amazon Web Services where he focuses on helping engineers, developers and data scientists solve challenging problems with machine learning. Prior to joining AWS, he worked at NVIDIA, MathWorks (makers of MATLAB & Simulink) and Oracle in product marketing and software development roles focused on machine learning. Shashank holds an M.S. in electrical engineering from Arizona State University.

Michael Radwin is Vice President of Data Science at Intuit, with responsibility for leading a team dedicated to using artificial intelligence and machine learning models for security, anti-fraud and risk. Prior to Intuit, Radwin was Vice President of Engineering of Anchor Intelligence, which used machine learning ensemble methods to fight online advertising fraud. He also served as Director of Engineering at Yahoo!, where he built ad-targeting and personalization algorithms with neural networks and naive Bayesian classifiers, and scaled web platform technologies, Apache and PHP. Radwin holds an ScB in Computer Science from Brown University.

Presentations

Design thinking is a methodology for creative problem solving developed at Stanford University d.school. The methodology is used by world-class design firms like IDEO and many of the world's leading brands like Apple, Google, Samsung, and GE. In this session, Michael Radwin, VP of Data Science at Intuit, will offer a recipe for how to apply design thinking to the development of AI/ML products.

Maithra Raghu is a PhD Candidate at Cornell University and a Research Scientist at Google Brain. Her research focuses on developing tools to understand deep neural networks and using these insights in healthcare applications. She has been named as one of the Forbes 30 Under 30 in Science, and an EECS Rising Star by MIT.

Presentations

With the fundamental breakthroughs in Artificial Intelligence and the significant increase of digital health data, there has been enormous interest in AI for healthcare applications. In this talk I present both how to more effectively develop AI algorithms for these settings and present the novel prediction challenges and successes arising from the interaction of AI algorithms and human experts.

Delip Rao is the vice president of research at the AI Foundation, where he leads speech, language, and vision research efforts for generating and detecting artificial content. Previously, he founded AI research consulting company Joostware and the Fake News Challenge, an initiative to bring AI researchers across the world to work on fact-checking related problems. Delip is the author of a recent book on deep learning and natural language processing. His attitude to production NLP research is shaped by the time he spent at Joostware working for enterprise clients, at Google, as the first machine learning researcher on the Twitter antispam team, and as an early researcher at Amazon Alexa.

Presentations

Delip Rao explores natural language processing with deep learning, walking you through neural network architectures and NLP tasks and teaching you how to apply these architectures for those tasks.

Shourabh is a Manager of Data Science in the data engineering organization at Trulia (Zillow Group). He has over 5 years of industry experience working in AI, deep learning, computer vision and personalization, deploying these systems to production at scale. Shourabh and his team focus on developing data science solutions to gain a better understanding of Trulia’s customers, specifically how they engage with content and property recommendations. Shourabh completed his Master’s degree from Carnegie Mellon University where he did research on “Event Detection in Consumer Videos,” applying deep learning on multi-modal (audio/images) data.

Presentations

360-degree images have become ubiquitous in industries ranging from real estate to travel. They enable an immersive experience that benefits consumers but creates a challenge for businesses: how do you direct viewers to the most important parts of the scene? In this session, attendees will learn to identify and extract engaging static 2D images using specific algorithms and deep learning methods.

Joy is a Data Scientist in Intuit’s Machine Learning Futures Group working on ML problems in limited label data settings. Joy holds a PhD from MIT, where she spent five years doing biological object tracking experiments, and modeling them using Markov Decision Processes.

Presentations

Document Understanding is a company-wide initiative at Intuit that aims to make data preparation and entry obsolete through the application of computer vision and machine learning. A team of Data Scientists will describe the design and modeling methodologies used to build this platform-as-a-service.

Brennan Saeta is a software engineer on the Google Brain team leading the Swift for TensorFlow project. He previously was the TensorFlow tech lead for Cloud TPUs.

Presentations

Swift for TensorFlow is a next-generation machine learning and differential programming framework that unlocks new domains and applications. This talk will dance through the motivations for Swift, the benefits of this toolchain, and how to use Swift for TensorFlow in your projects.

Alejandro is the Chief Scientist at the Institute for Ethical AI & Machine Learning, where he leads highly technical research on machine learning explainability, bias evaluation, reproducibility and responsible design. With over 10 years of software development experience, Alejandro has held technical leadership positions across hyper-growth scale-ups and tech giants including Eigen Tchnologies, Bloomberg LP and Hack Partners. He has a strong track record building departments of machine learning engineers from scratch, and leading the delivery of large-scale machine learning system across the financial, insurance, legal, transport, manufacturing and construction sectors (in Europe, US and Latin America).

Presentations

In this talk we demystify AI explainability through a practical hands-on case study. Our objective will be to automate a loan approval process by building and evaluating a deep learning model. We'll introduce motivations through the practical risks that arise with "undesired bias" & "black box models", and we will show tackle these challenges using tools from latest research and domain knowledge.

Robert Schroll is a data scientist in residence at the Data Incubator. Previously, he held postdocs in Amherst, Massachusetts, and Santiago, Chile, where he realized that his favorite parts of his job were teaching and analyzing data. He made the switch to data science and has been at the Data Incubator since. Robert holds a PhD in physics from the University of Chicago.

Presentations

The TensorFlow library provides for the use of computational graphs, with automatic parallelization across resources. This architecture is ideal for implementing neural networks. This training will introduce TensorFlow's capabilities in Python. It will move from building machine learning algorithms piece by piece to using the Keras API provided by TensorFlow with several hands-on applications.

Nagendra leads the Analytics Product Development initiative for EXL. He has over 17 years of experience in developing advanced analytics solutions across business functions. His focus has been on developing solutions that enable better decision making through the use of Machine Learning, Natural Language Processing and Big Data technologies. Nagendra consults with senior executives of global firms across industry – including healthcare, insurance, banking, retail, and travel. Nagendra holds an MS degree from Purdue University, IN and a B.Tech. from IIT Bombay. At EXL, Nagendra has written thought leadership articles on healthcare clinical solutions and AI.

Presentations

Every NLP based document processing solution depends on converting scanned documents/ images to machine readable text using an OCR solution. However, accuracy of OCR solutions is limited by quality of scanned images. We show that generative adversarial networks can be used to bring significant efficiencies in any document processing solution by enhancing resolution and de-noising scanned images.

I use machine learning and natural language processing to solve problems with clinical data.

Presentations

We discuss the use of deep learning models to perform natural language inference, a fundamental task in natural language processing. We introduce a recently released dataset for this task in the clinical domain, describe state of the art models and what can be done to adapt these into the healthcare domain, and finally discuss applications that can leverage these models.

Guoqiong Song is a senior deep learning software engineer of the big data technology team at Intel. Her interest is in developing and optimizing distributed deep learning algorithms on Spark. She has a PhD in atmospheric and oceanic sciences from UCLA with a focus on numerical modeling and optimization.

Presentations

Jason Dai, Yuhao Yang, Jennie Wang, and Guoqiong Song explain how to build and productionize deep learning applications for big data with Analytics Zoo—a unified analytics and AI platform that seamlessly unites Spark, TensorFlow, Keras, and BigDL programs into an integrated pipeline—using real-world use cases from JD.com, MLSListings, the World Bank, Baosight, and Midea/KUKA.

Evan Sparks is cofounder and CEO of Determined AI, a software company that makes machine learning engineers and data scientists fantastically more productive. Previously, Evan worked in quantitative finance and web intelligence. He holds a PhD in computer science from UC Berkeley, where, as a member of the AMPLab, he contributed to the design and implementation of much of the large-scale machine learning ecosystem around Apache Spark, including MLlib and KeystoneML. He also holds an AB in computer science from Dartmouth College.

Presentations

We describe the current gap between the AI haves: Google, Facebook, Amazon, and Microsoft, and the AI have-nots: the rest of the industry, from the perspective of software infrastructure for model development. We discuss opportunities for end-to-end system design to enable rapid iteration and scale in AI application development.

Kenneth O. Stanley is Charles Millican Professor of Computer Science at the University of Central Florida and director there of the Evolutionary Complexity Research Group. He was also a co-founder of Geometric Intelligence Inc., which was acquired by Uber to create Uber AI Labs, where he is now also a senior research science manager and head of Core AI research. He received a B.S.E. from the University of Pennsylvania in 1997 and received a Ph.D. in 2004 from the University of Texas at Austin. He is an inventor of the Neuroevolution of Augmenting Topologies (NEAT), HyperNEAT, and novelty search neuroevolution algorithms for evolving complex artificial neural networks. His main research contributions are in neuroevolution (i.e. evolving neural networks), generative and developmental systems, coevolution, machine learning for video games, interactive evolution, and open-ended evolution. He has won best paper awards for his work on NEAT, NERO, NEAT Drummer, FSMC, HyperNEAT, novelty search, and Galactic Arms Race. His original 2002 paper on NEAT also received the 2017 ISAL Award for Outstanding Paper of the Decade 2002 – 2012 from the International Society for Artificial Life. He is a coauthor of the popular science book, “Why Greatness Cannot Be Planned: The Myth of the Objective” (published by Springer), and has spoken widely on its subject.

Presentations

Ion Stoica is a professor in the EECS Department at the University of California, Berkeley, where he does research on cloud computing and networked computer systems. Ion’s previous work includes dynamic packet state, chord DHT, internet indirection infrastructure (i3), declarative networks, and large-scale systems, including Apache Spark, Apache Mesos, and Alluxio. He is the cofounder of Databricks—a startup to commercialize Apache Spark—and Conviva—a startup to commercialize technologies for large-scale video distribution. Ion is an ACM fellow and has received numerous awards, including inclusion in the SIGOPS Hall of Fame (2015), the SIGCOMM Test of Time Award (2011), and the ACM doctoral dissertation award (2001).

Presentations

Ray is a general purpose framework for programming your cluster. We will lead a deep dive into Ray, walking you through its API and system architecture and sharing application examples, including several state-of-the-art AI algorithms.

Roshan Sumbaly leads computer vision efforts at Facebook focused on visual people understanding and infrastructure. Prior to that he led various teams at Coursera and LinkedIn, working on data products and infrastructure.

Presentations

There aren't many systems in the world that need to run hundreds of computer vision models (from classification to segmentation) on billions of visual entities (images, videos, 3D) daily. This talk walks through the challenges we faced while building such a platform and how, surprisingly, a lot of the answers were found in traditional software engineering best practices.

David Talby is a chief technology officer at Pacific AI, helping fast-growing companies apply big data and data science techniques to solve real-world problems in healthcare, life science, and related fields. David has extensive experience in building and operating web-scale data science and business platforms, as well as building world-class, Agile, distributed teams. Previously, he was with Microsoft’s Bing Group, where he led business operations for Bing Shopping in the US and Europe, and worked at Amazon both in Seattle and the UK, where he built and ran distributed teams that helped scale Amazon’s financial systems. David holds a PhD in computer science and master’s degrees in both computer science and business administration.

Presentations

New AI solutions in question answering, chatbots, structured data extraction, text generation, and inference all require deep understanding of the nuances of human language. David Talby shares challenges, risks, and best practices for building NLU-based systems, drawing on examples and case studies from products and services built by Fortune 500 companies and startups over the past seven years.

Jasjeet Thind is the vice president of artificial intelligence at Zillow. His group focuses on machine-learned prediction models and big data systems that power use cases such as Zestimates, personalization, housing indices, search, content recommendations, and user segmentation. Prior to Zillow, Jasjeet served as director of engineering at Yahoo, where he architected a machine-learned real-time big data platform leveraging social signals for user interest signals and content prediction. The system powers personalized content on Yahoo, Yahoo Sports, and Yahoo News. Jasjeet holds a BS and master’s degree in computer science from Cornell University.

Presentations

Advances in AI & deep learning are enabling new technologies to mimic how the human brain interprets scenes, objects & images. This progress has major implications for businesses that need to extract meaning from overwhelming quantities of unstructured data. In this session, learn how implementing computer vision based in deep neural networks allows machines to “see” images in an entirely new way.

Skyler Thomas is an engineer at MapR, where he is designing Kubernetes-based infrastructure to deliver machine learning and big data applications at scale. Previously, Skyler was an architect at IBM, where he worked with more than a hundred customers to deliver extreme-scale applications in the healthcare, financial services, and retail industries.

Presentations

The popular open source Kubeflow project is one of the best ways to start doing machine learning and AI on top of Kubernetes. However, Kubeflow is a huge project with dozens of large complex components. In this hands-on session, we will learn about the Kubeflow components and how they interact with Kubernetes. We explore the machine learning lifecycle from model training to model serving.

Solmaz Torabi is a Data Scientist at EXL Service. She holds a PhD in Electrical and Computer Engineering from Drexel University. At EXL, she is responsible for building image and text analytics models using deep learning methods to extract information from images and documents.

Presentations

Every NLP based document processing solution depends on converting scanned documents/ images to machine readable text using an OCR solution. However, accuracy of OCR solutions is limited by quality of scanned images. We show that generative adversarial networks can be used to bring significant efficiencies in any document processing solution by enhancing resolution and de-noising scanned images.

After receiving his PhD in Physics, TJ began working in industry as an applied ML researcher. His previous work includes building fashion recommendation models using computer vision to help understand visual style at Stitch Fix, as well as building models to help automatically analyze issues with sign-up conversion at Netflix. Now, at Intuit, he works on the ML Futures team tackling research problems in the areas of CV and NLP in order to better customer experience within Intuit’s core products.

Presentations

Document Understanding is a company-wide initiative at Intuit that aims to make data preparation and entry obsolete through the application of computer vision and machine learning. A team of Data Scientists will describe the design and modeling methodologies used to build this platform-as-a-service.

Anusua Trivedi is a Senior Data Scientist Lead at Microsoft. She works on “AI for Good” – developing advanced Deep Learning models & AI solutions for humanitarian causes. Her focus is AI for Healthcare where she explores how AI can help make healthcare more affordable and accessible to everyone around the world. Prior to joining Microsoft, Anusua has held positions with UT Austin and University of Utah. Anusua is a frequent speaker at machine learning and AI conferences.

Presentations

In this session, we capture a comprehensive study of existing text transfer learning literature in the research community. We explore popular Machine Reading Comprehension (MRC) algorithms. We evaluate and compare the performance of transfer learning approach for creating a QA system for a book corpus using the pretrained MRC models.

Manasi Vartak is the founder and CEO of Verta.AI an early-stage startup building software to help data science and machine learning teams rapidly build and integrate ML across products. Manasi is the creator of ModelDB, the first open-source model management system that is used at Fortune 500 companies and in popular open-source projects including KubeFlow. Manasi earned her Ph.D. in computer science from MITCSAIL where she worked on software systems for data science and ML. Besides ML Infra, Manasi has worked on personalizing the Twitter News feed, automated data visualization, and ML model debugging. She is a recipient of the Facebook Ph.D. Fellowship and the Google Anita Borg Scholarship.

Presentations

Enterprises are investing heavily in integrating AI/ML into their business, and yet it remains challenging to transform these research-oriented initiatives into revenue driving functions due to a lack of efficient tooling. We discuss key methods that enterprise AI teams can leverage with regards to driving revenue including A/B testing, data pipelines, reproducibility.

Dean Wampler is the vice president of fast data engineering at Lightbend, where he leads the creation of the Lightbend Fast Data Platform, a distribution of scalable, distributed stream processing tools including Spark, Flink, Kafka, and Akka, with machine learning and management tools. Dean is the author of Programming Scala and Functional Programming for Java Developers and the coauthor of Programming Hive, all from O’Reilly. He is a contributor to several open source projects. A frequent Strata speaker, he’s also the co-organizer of several conferences around the world and several user groups in Chicago.

Presentations

This hands-on tutorial examines production use of ML in streaming data pipelines; how to do periodic model retraining and low-latency scoring in live streams. We'll discuss Kafka as the data backplane, pros and cons of microservices vs. systems like Spark and Flink, tips for Tensorflow and SparkML, performance considerations, model metadata tracking, and other techniques.

Bin is a Principal Software Engineering Manager in AI and Research group of Microsoft. Since joining Microsoft in 2014, He is currently the tech manager in Multi-tenancy team, and the go-to person across the entire Platform team in this area. He has initiated key efforts to improve the stability of Yarn, which now is deployed to 30k+ machines, supporting 30P+ cold data. He is also leading efforts in supporting model training such as ChaNa and LR/MCLR on Yarn, which has contributed to Ads Selection, PA, MM, Adinsight, Relevance etc. Also, he lead the team to support Linux workloads on Windows by extending Yarn to support on demand VM lifecycle provisioning. The MT effort now is extending to other key AIR scenarios such as image processing, DR, Malta data processing, bot trainer, etc. Bin is also leading the development of OSS DL training platform OpenPAI which is specifically designed to be user friendly and extensible for various DL training frameworks and can runs on on-premises as well as cloud environments.

Presentations

FrameworkLauncher is built to orchestrate all kinds of workloads on YARN through the same interface without making changes to the workload themselves.
These workloads include but not limited to: Large-Scale Long-Running Services (DeepLearning Serving, HBase, Kafka, etc), Batch Jobs (DeepLearning Training, KDTree Building, etc) and Streaming Jobs (Data Processing, Dynamic Rendering, etc).

Bio:

Haixun Wang is VP of Engineering and Distinguished Scientist at WeWork. He is an IEEE fellow and Editor in Chief of the IEEE Data Engineering Bulletin. Before joining WeWork, he was a Director of Natural Language Processing at Amazon. From 2015 to 2017, he led the NLP organization in Facebook working on query and document understanding. From 2013 to 2015, he was with Google Research, working on natural language processing. From 2009 to 2013, he led research in knowledge bases, graph systems, and text processing at Microsoft Research Asia. He had been a research staff member at IBM T. J. Watson Research Center from 2000 – 2009. He was Technical Assistant to Stuart Feldman (Vice President of Computer Science of IBM Research) from 2006 to 2007, and Technical Assistant to Mark Wegman (Head of Computer Science of IBM Research) from 2007 to 2009. He received the Ph.D. degree in Computer Science from the University of California, Los Angeles in 2000. He has published more than 200 research papers in international journals and conference proceedings. He served as PC chairs of many academic conferences, and he is on the editorial board of journals such as IEEE Transactions of Knowledge and Data Engineering (TKDE) and Journal of Computer Science and Technology (JCST). He won the best paper award in ICDE 2015, 10-year best paper award in ICDM 2013, and best paper award of ER 2009.

Presentations

The AI advancements in the cyber world far surpass those in the physical world. This discussion will outline how WeWork aims to change this by discussing the approaches the company is taking to bring AI to the real world, ranging from modeling a neighborhood to creating digital twins of a building, and how AI can make businesses more efficient and improve people’s quality of life.

Hui Wang is a Staff Data Scientist at Intuit. Hui has a PhD in Chemical Engineering from Yale. Prior to Intuit, he conducted fundamental NLP research with grants from NIST and the CIA, and provided data modeling for investment banks and hedge funds.

Presentations

Document Understanding is a company-wide initiative at Intuit that aims to make data preparation and entry obsolete through the application of computer vision and machine learning. A team of Data Scientists will describe the design and modeling methodologies used to build this platform-as-a-service.

Jiao (Jennie) Wang is a software engineer on the big data technology team at Intel, where she works in the area of big data analytics. She’s engaged in developing and optimizing distributed deep learning framework on Apache Spark.

Presentations

Jason Dai, Yuhao Yang, Jennie Wang, and Guoqiong Song explain how to build and productionize deep learning applications for big data with Analytics Zoo—a unified analytics and AI platform that seamlessly unites Spark, TensorFlow, Keras, and BigDL programs into an integrated pipeline—using real-world use cases from JD.com, MLSListings, the World Bank, Baosight, and Midea/KUKA.

Dr. Jisheng Wang has 10+ years of experience applying state-of-the-art big data and data science technologies to solve challenging enterprise problems including: security, networking and IoT. He is currently the Head of Data Science at Mist Systems, and leads the development of Marivs – the first AI-driven virtual network assistant that automates the visibility, troubleshooting, reporting and maintenance of enterprise networking.

Before joining Mist, Jisheng worked as the Senior Director of Data Science in the CTO office of Aruba, a Hewlett-Packard Enterprise company since its acquisition of Niara in February 2017. As the Chief Scientist at Niara, Jisheng led the overall innovation and development effort in big data infrastructure and data science. He also invented the industry’s first modular and data-agonistic User and Entity Behavior Analytics (UEBA) solution, which is widely deployed today among global enterprises. Before that, Jisheng was a technical lead in Cisco responsible for various security products.

Jisheng received his Ph.D. in Electric Engineering from Penn State University, and is also a frequent speaker at AI/ML conferences, including: O’Reilly Strata AI, Frontier AI, Spark Summit, Hadoop Summit and BlackHat.

Presentations

Increased complexity and business demands continue to make enterprise network operation more challenging. In this talk, we will introduce the architecture of the first autonomous network operation solution together with two examples of ML-driven automated actions. We also share experiences and lessons learned applying ML/DL and AI to the development of SaaS-based enterprise solutions.

Yuqi Wang is a Software Engineer in AI and Research group of Microsoft. He has 3 years of experience in Apache YARN, Container Orchestration, and AI Infrastructure. He is the author and maintainer for Microsoft FrameworkLauncher which is built to orchestrate all kinds of workloads through the same interface without making changes to the workload themselves. Besides, he has internally contributed several features into YARN to support Long-Running Service better on Windows. Currently he is still working on the FrameworkLauncher to support AI workloads better and running natively on Kubernetes.

Presentations

FrameworkLauncher is built to orchestrate all kinds of workloads on YARN through the same interface without making changes to the workload themselves.
These workloads include but not limited to: Large-Scale Long-Running Services (DeepLearning Serving, HBase, Kafka, etc), Batch Jobs (DeepLearning Training, KDTree Building, etc) and Streaming Jobs (Data Processing, Dynamic Rendering, etc).

Mark Weber (@markrweber) is a research scientist at the MIT-IBM Watson AI Lab. His expertise is connecting dots across disciplines to develop emergent technologies for positive real-world impact. Mark’s current work involves the development of new graph analytics methods for anti-money laundering (see https://www.markrweber.com/graph-deep-learning/).

Mark cut his teeth at the MIT Media Lab working at the Digital Currency Initiative, where he led the development of the b_verify protocol for publicly verifiable records, focused on warehouse receipts in agricultural supply chains. Papers and open-source code here: https://www.markrweber.com/b_verify/. Mark earned an MBA in finance from MIT Sloan, where he was a Fellow at the Legatum Center for Entrepreneurship & Development.

Prior to MIT, Mark produced documentary films on political economy and development, most notably a film called Poverty, Inc., winner of over 50 film festival honors and the $100,000 Templeton Freedom Award (available on Netflix and other platforms via www.povertyinc.org).

As a public speaker, Mark enjoys opportunities to share his research and learn from others. He has delivered talks at over 100 top universities, organizations, and events around the world.

Presentations

Organized crime inflicts human suffering on a genocidal scale: upwards of 700,000 people per year are "exported" in a $40 billion human trafficking industry enslaving an estimated 40 million people. Such nefarious industries rely on sophisticated money laundering schemes to operate. A new field of AI called graph convolutional networks can help.

Xiao Xiao is a Data Scientist in Intuit’s Consumer Group, using ML to enhance customer experience. Xiao holds a PhD in Ecology and a MS in Statistics, where she applied statistical analysis to study ecological patterns at broad spatial and temporal scales.

Presentations

Document Understanding is a company-wide initiative at Intuit that aims to make data preparation and entry obsolete through the application of computer vision and machine learning. A team of Data Scientists will describe the design and modeling methodologies used to build this platform-as-a-service.

Yuhao Yang is a senior software engineer on the big data team at Intel, where he focuses on deep learning algorithms and applications—particularly distributed deep learning and machine learning solutions for fraud detection, recommendation, speech recognition, and visual perception. He’s also an active contributor to Apache Spark MLlib.

Presentations

Machine learning (ML) and deep learning (DL) projects are becoming increasingly common at enterprises and startups alike and have been a key innovation engine for Amazon businesses such as Go, Alexa, and Robotics. In this 2 day training, Wenming Ye (AWS) and Miro Enev (Nvidia) offer a practical next step in DL learning with instructions, demos, and hands-on labs.

Ting-Fang Yen is director of research at DataVisor, the leading fraud detection platform powered by transformational AI technology. Her work applies big data analytics and machine learning to tackle problems in cybersecurity. Ting-Fang holds a PhD in electrical and computer engineering from Carnegie Mellon University.

Presentations

We describe a monitor for production machine learning systems that handle billions of requests daily. Our approach discovers detection anomalies, such as spurious false positives, as well as gradual concept drifts when the model no longer captures the target concept. This session presents new tools for detecting undesirable model behaviors early in large-scale online ML systems.

Greg Zaharchuk is a radiologist and professor in radiology at Stanford University and a neuroradiologist at Stanford Hospital. His research interests include deep learning applications in neuroimaging, imaging of cerebral hemodynamics with MRI and CT, noninvasive oxygenation measurement with MRI, clinical imaging of cerebrovascular disease, imaging of cervical artery dissection, MR/PET in neuroradiology, and resting-state fMRI for perfusion imaging and stroke.

Presentations

Subtle Medical provides AI solutions, cleared by FDA and powered by industry framework, such as Intel OpenVINO, to deliver 4x-10x faster MRI scans, 4x faster PET scans and up to 10x dosage reduction. Clinical evaluation at hospitals such as Hoag hospital, UCSF, and Stanford demonstrates the significant and immediate values of AI to improve the productivity of healthcare workflow.

Yi Zhang is the CTO of Rulai. She has been a consultant or technical adviser for enterprises (HP, Toyota, Alibaba, etc.) and startups.

Dr. Yi Zhang is also a tenured Professor in Computer Science and Engineering Department, University of California, Santa Cruz. She has more than 20 years of experience in AI, with various awards, including ACMSIGIR Best Paper Award, National Science Foundation Faculty Career Award, Google Research Award and Microsoft Research Award. She has served as program chair, area chair and PC member for various top tier international conferences. Dr. Zhang received her Ph.D. from School of Computer Science at Carnegie Mellon University.

Presentations

This talk will present the predictions represent our thoughts on how conversational technology will evolve from its current state in 2019. Common misunderstandings about the technologies and case studies in several industries will be presented and discussed.

Huaixiu Zheng is a Senior Data Scientist at Uber, where he’s a major contributor to several ongoing efforts at Uber using deep learning-based NLP, ML, and AI technologies to empower the intelligent business operations. Huaixiu has made significant contributions in the fields of quantum waveguide QED, quantum phase transition in dissipative environments, and photonic quantum computation. Previously, he was a postdoctoral researcher at Yale University, where he worked on quantum error corrections and topological quantum computation. He has published more than 25 journal and conference papers in prestigious journals such as Nature, Nature Physics, and Physical Review Letters, and has more than 1,000 citations. He received prestigious academic and industrial awards, including the Chinese Government Award for Outstanding Self-Financed Students Abroad, the John T. Chambers Scholarship, a second-place award from the SPIE-AAPM-NCI Prostate MR Classification Challenge, a second-place award for the SPIE-AAPM-NCI Prostate MR Gleason Grade Group Challenge, and the second prize (as part of team Future Lifecare) of the 8th Intelligent System Summit & TEEC Cup Startup Contest. He holds a PhD in quantum physics and quantum computation from Duke University.

Presentations

In this talk, I will cover how Uber applies Deep Learning in the domain of NLP and Conversational AI. In particular, I will go into details of how we implement AI solutions in a real-world environment, as well as cutting edge research we are doing in end-to-end dialogue systems.

Huaiyu Zhu is a member of the Infrastructure for Intelligent Information Systems group at IBM Research – Almaden. His research interests includes natural language processing, machine learning and statistics, and scalable information systems. He has worked on neural networks, information geometry, text analytics, information extraction, enterprise search, knowledge discovery, enterprise analytics platforms, and multilingual natural language processing. He has a PhD in Computational Mathematics and Statistics from University of Liverpool.

Presentations

Natural Language Understanding (NLU) underlies a wide range of applications and services. Rich resources available for English do not exist for most other languages. Is it possible to avoid duplicating the effort? Further, can NLU-dependent applications be developed language-agnostically (write once, applicable to multiple languages)? We will show a vision to answering yes to both questions.

Shelley Zhuang is founder and managing partner at 11.2 Capital. Shelley has over 15 years of experience in technology as a software engineer, research scientist, business executive, and venture capitalist. Previously, Shelley was EVP of business development at Ecoplast Technologies, where she oversaw business development and sales efforts in North America, and a principal at DFJ, where she was actively involved in a number of investments, including Ecoplast Technologies, FeedBurner (acquired by Google for $100M), Flurry (acquired by Yahoo for $240M), PPLive (acquired by Suning for $420M), TicketsNow (acquired by Ticketmaster for $265M), Xfire (acquired by Viacom for $102M), YeePay. Shelley is a techie at heart. She is currently an advisor at Skydeck and ML7 associate at Creative Destruction Lab. She also served on Enigma 2016’s program committee. Shelley holds a BS in computer science and computer engineering from the University of Missouri and a PhD in computer science from the University of California, Berkeley.

Presentations

How is artificial intelligence is transforming drug discovery and development

Yoav Zimmerman is a software engineer at Determined AI, where he works closely with leading organizations to help them apply deep learning successfully using Determined’s cutting-edge software. Prior to Determined AI, Yoav worked on knowledge representation at Google. Yoav holds a B. Sc. from UCLA.

Presentations

Success with deep learning requires understanding more than just TensorFlow or Keras. In this tutorial, we will describe a range of practical problems faced by DL practitioners and the software tools and techniques needed to address them, including data prep/augmentation, GPU scheduling, hyperparameter tuning, distributed training, metrics management, deployment, and mobile/edge optimization.